PIRL [1]} is one of the works [2]}, [3]} that questioned the intuition behind pretext covariant representation learning. The authors argued that representations ought to be invariant under image transformations because the visual semantics remain the same under most of the image transformations [4]}, [5]}, e.g. a rotated image of dog still contains a dog. Thus, a representation that is invariant to image transformations that do not alter visual semantics should be robust.
The spirit of PIRL is summarized in Fig. REF  (a), where the standard pretext covariant learning methods and the pretext invariant learning methods are compared. Instead of predicting the representation to be representative of the property of the transformation, PIRL learns its representation to be representative of the visual semantics that are invariant of the transformation.
<FIGURE>